mirror of
https://github.com/escalante29/healthy-fit.git
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184c8330a7
Introduces DSPy-based nutrition and plan generation modules, including image analysis for nutritional info and personalized diet/exercise plans. Adds new API endpoints for health metrics/goals, nutrition image analysis, and plan management. Updates models, schemas, and backend structure to support these features, and includes initial training data and configuration for prompt optimization.
108 lines
4.2 KiB
Python
108 lines
4.2 KiB
Python
import base64
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import dspy
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from pydantic import BaseModel, Field
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from app.config import settings
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class NutritionalInfo(BaseModel):
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reasoning: str = Field(description="Step-by-step reasoning for the nutritional estimates")
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name: str = Field(description="Name of the food item")
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calories: float = Field(description="Estimated calories")
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protein: float = Field(description="Estimated protein in grams")
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carbs: float = Field(description="Estimated carbohydrates in grams")
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fats: float = Field(description="Estimated fats in grams")
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micros: dict | None = None
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class ExtractNutrition(dspy.Signature):
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"""Extract nutritional information from a food description.
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You must first provide a detailed step-by-step reasoning analysis of the ingredients,
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portions, AND preparation methods (cooking oils, butter, sauces) before estimating values.
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Verify if the caloric totals match the sum of macros (multiplying protein/carbs by 4, fats by 9).
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"""
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description: str = dspy.InputField(desc="Description of the food or meal")
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nutritional_info: NutritionalInfo = dspy.OutputField(desc="Nutritional information with reasoning")
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class AnalyzeFoodImage(dspy.Signature):
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"""Analyze the food image to estimate nutritional content.
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1. Identify all food items and estimated portion sizes.
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2. CRITICAL: Account for hidden calories from cooking fats, oils, and sauces (searing, frying).
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3. Reason step-by-step about the total composition before summing macros.
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"""
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image: dspy.Image = dspy.InputField(desc="The food image")
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description: str = dspy.InputField(desc="Additional user description", default="")
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nutritional_info: NutritionalInfo = dspy.OutputField(desc="Nutritional information with reasoning")
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class NutritionModule(dspy.Module):
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def __init__(self):
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super().__init__()
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self.extract = dspy.ChainOfThought(ExtractNutrition)
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self.analyze_image = dspy.ChainOfThought(AnalyzeFoodImage)
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# Load optimized prompts if available
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import os
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compiled_path = os.path.join(os.path.dirname(__file__), "nutrition_compiled.json")
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if os.path.exists(compiled_path):
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self.load(compiled_path)
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print(f"Loaded optimized DSPy prompts from {compiled_path}")
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else:
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print("No optimized prompts found, using default zero-shot.")
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def forward(self, description: str):
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pred = self.extract(description=description)
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# Assertion: Check Macro Consistency
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calc_cals = (
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(pred.nutritional_info.protein * 4) + (pred.nutritional_info.carbs * 4) + (pred.nutritional_info.fats * 9)
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)
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# dspy.Suggest is not available in dspy>=3.1.0
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# dspy.Suggest(
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# abs(calc_cals - pred.nutritional_info.calories) < (pred.nutritional_info.calories * 0.20),
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# f"The sum of macros ({calc_cals:.1f}) should match the total calories "
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# f"({pred.nutritional_info.calories}). Check your math.",
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# )
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return pred
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def forward_image(self, image_url: str, description: str = ""):
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image = dspy.Image(image_url)
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pred = self.analyze_image(image=image, description=description)
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# Assertion: Check Macro Consistency
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calc_cals = (
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(pred.nutritional_info.protein * 4) + (pred.nutritional_info.carbs * 4) + (pred.nutritional_info.fats * 9)
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)
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# dspy.Suggest is not available in dspy>=3.1.0
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# dspy.Suggest(
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# abs(calc_cals - pred.nutritional_info.calories) < (pred.nutritional_info.calories * 0.20),
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# f"The sum of macros ({calc_cals:.1f}) should match the total calories "
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# f"({pred.nutritional_info.calories}). Check your math.",
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# )
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return pred
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nutrition_module = NutritionModule()
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def analyze_nutrition_from_image(image_bytes: bytes, description: str = "") -> NutritionalInfo:
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if not settings.OPENAI_API_KEY:
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raise ValueError("OpenAI API Key not set")
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# Convert to base64 data URI
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base64_image = base64.b64encode(image_bytes).decode("utf-8")
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image_url = f"data:image/jpeg;base64,{base64_image}"
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# Use DSPy module
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result = nutrition_module.forward_image(image_url=image_url, description=description)
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return result.nutritional_info
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